Another area of research in glaciology that employs Bayesian methods stems from the computer-model calibration literature [1]}. Here, the aim is to tune, or calibrate, numerical models from data in a Bayesian framework. An important consideration in Bayesian calibration is the characterisation of numerical-model discrepancy. Briefly, model discrepancy is a model term used to capture the difference between the output of a computer model (usually the output of the computer model using the best fitting parameter value) and the true value of the process being modelled. Model discrepancy has been shown to be necessary in order to obtain realistic inferences of numerical-model parameters [1]}, [3]}.
